SGraphZoe: Explainable self-supervised framework for signal-based anomaly detection

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@InProceedings{Kamalov:2023:DSP,

• author = "Mikhail Kamalov and Luca Uggeri and Ingrid Grenet and Jonathan Daeden",
• booktitle = "2023 24th International Conference on Digital Signal Processing (DSP)",
• title = "{SGraphZoe:} Explainable self-supervised framework for signal-based anomaly detection",
• year = "2023",
• abstract = "Signal-based anomaly detection is a recurring problem that has drawn the attention of many research projects and resulted in the development of multiple solutions. One of the main obstacles to anomaly detection is the rarity of the occurrences of interest. Extremely small amount of labelled data is troublesome from the training perspective since it has a detrimental influence on the accuracy of predictions. The second challenge is providing a clear and understandable model. Answering this second issue is particularly important for a variety of industries since it is beneficial to understand what causes outliers in order to avoid them in the future. To address the aforementioned concerns, we propose a novel self-supervised framework named SGraphZoe which outperforms linear semi-supervised state-of-the-art outlier detection algorithms while maintaining transparency throughout training and prediction steps. This framework is built on a Self-supervised strategy and combines a semi-supervised (Graph Diffusion & PCA) and a supervised (Zoetrope Genetic Programming) algorithms.",
• keywords = "genetic algorithms, genetic programming, Training, Industries, Signal processing algorithms, Digital signal processing, Prediction algorithms, Mathematical models",
• DOI = "doi:10.1109/DSP58604.2023.10167944",
• ISSN = "2165-3577",
• month = jun,
• notes = "Also known as \cite{10167944}",

}

Genetic Programming entries for Mikhail Kamalov Luca Uggeri Ingrid Grenet Jonathan Daeden

Citations